Falls are a common problem among older adults, including those who are relatively healthy and living independently. Exercise has been recommended as an intervention to reduce falls by slowing and/or reversing age-related declines in balance, strength, and mobility. However, it remains unclear which types or combinations of programs are most effective. The objective of this study was to investigate whether exercise programs performed by healthy older adults were associated with superior balance, strength, and functional mobility measures that are pertinent to fall prevention. This study compared three distinct groups: participants of a balance- and strength-focused training program (i.e., Better Bones and Balance®), participants engaged in a general walking program, and sedentary individuals. Balance was measured using the Sensory Organization Test composite score and sensory ratios. Isometric strength of the lateral hip stabilizers (i.e., abductors and adductors) was measured in terms of maximum voluntary contraction and rapid torque production. Rapid torque measures included contractile impulse and rate of torque development evaluated at 0-100 ms and 0-300 ms from contraction onset. Functional mobility was measured by the time to complete the Four Square Step Test. Hip abduction contractile impulse (0-300 ms) was 1.905 Nm*s and 1.539 Nm*s higher for the Better Bones and Balance (BBB) group compared to the walking and sedentary groups, respectively. No differences were found among the groups for any of the hip adduction torque measures or Sensory Organization Test balance scores. The BBB group completed the Four Square Step Test faster than the walking and sedentary groups by 0.90 s and 1.06 s, respectively. In conclusion, participation in the balance- and strength-focused training program was associated with superior performance in some measures of strength and functional mobility that may be important for fall prevention. / Graduation date: 2013

Indiana University-Purdue University Indianapolis (IUPUI) / Physical activity monitoring using wearable sensors give valuable information about patient's neuro activities. Fall among ages of 60 and older in US is a leading cause for injury-related health issues and present serious concern in the public health care sector. If the emergency treatments are not on time, these injuries may result in disability, paralysis, or even death. In this work, we present an approach that early detect fall occurrences. Low power capacitive accelerometers incorporated with microcontroller processing units were utilized to early detect accurate information about fall events. Decision tree algorithms were implemented to set thresholds for data acquired from accelerometers. Data is then verified against their thresholds and the data acquisition decision unit makes the decision to save patients from fall occurrences. Daily activities are logged on an onboard memory chip with Bluetooth option to transfer the data wirelessly to mobile devices. In this work, a system prototype based on neurosignal activities was built and tested against seven different daily human activities for the sake of differentiating between fall and non-fall detection. The developed system features low power, high speed, and high reliability. Eventually, this study will lead to wearable fall detection system that serves important need within the health care sector. In this work Inter-Integrated Circuit (I2C) protocol is used to communicate between the accelerometers and the embedded control system. The data transfer from the Microcontroller unit to the mobile device or laptop is done using Bluetooth technology.